Looking to list your PhD opportunities? Log in here.
This project is no longer listed on FindAPhD.com and may not be available.
Click here to search FindAPhD.com for PhD studentship opportunitiesAbout the Project
Background
This PhD research studentship will contribute to the research in predicting high resource use in health services via explainable AI models
A Royal College of Emergency Medicine guideline highlights that there is consistent evidence that Frequent Attenders for a department do not constitute a stable cohort. A small number of ‘very high frequency attenders’ may have different characteristics to frequent attenders who are comprised of a more stable population with a lower admission rate. Within Lanarkshire, the data indicate that the frequent attenders seen are predominantly a younger cohort (i.e. not frail elderly), vulnerable and facing the multiple challenges of deprivation and poor health, both physical and mental.
Recommendations highlight the need for a more anticipatory and predictive approach to the identification and management of frequent attendance – moving from a system that is reactive, dealing with events once thresholds are met to one that can identify a person's risk of becoming a frequent attender early in their trajectory. Such intelligence provides information to inform interventions to provide early support, including the development of an evidence based multidisciplinary / multiagency model that leads to early management in the community and reduce unscheduled service use for these individuals and improve care for patients. This research is therefore to develop and test new explainable AI approaches to predict frequent attenders in NHS Lanarkshire.
Research Themes
The student will be asked to focus PhD research to target the use of state-of-the-art AI models to support the prediction and human centric analysis of frequent attendance. The specific objectives are:
- To conduct requirement analysis and engage stakeholders in the co-design of the solution for clinical practice;
- To apply explainable AI prediction model that incorporates causality structure learning together with uncertainty estimation to support prediction and human centric analysis of FA in healthcare systems
- To validate the new technologies, test their acceptability and usability at clinical sites and explore key stakeholder perceptions
We are in collaborations with a number of clinical settings with NHS Lanarkshire.
Eligibility
· The studentships are open to all the home, EU and International students.
· 36- month stipend, home tuition, travel and equipment are covered for successful candidates
· Candidates need to meet the university admission criteria, which can be found in the submission link below.
Application Submission
To apply for this, follow the official link. Please include a research proposal which describes your research plan.
https://www.strath.ac.uk/studywithus/postgraduateresearch/
Cut-off Date: 6th January 2023
Contact: For further enquiries, please contact Prof. Feng Dong, [Email Address Removed]

Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Glasgow, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs

PhD suggestions
Based on your current search criteria we thought you might be interested in these.
IAHS STUDENTSHIP: Predicting the trajectories of health care resource utilisation and risks of exacerbations in Chronic Obstructive Pulmonary Disease (COPD) using linked electronic health records
Aberdeen University
The use of green tea strategies combined with exercise training to promote enhanced fat oxidation and cardio-metabolic health: part 1
Anglia Ruskin University ARU
The Use of Technology to Support and Enhance Community Pharmacy Services in view of the COVID19 pandemic
Kingston University